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If you are new to Azure Storage and the Table service, first read Introduction to Microsoft Azure Storage and Get started with Azure Table Storage using . Although the focus of this guide is on the Table service, it will include some discussion of the Azure Queue and Blob services, and how you might use them along with the Table service in a solution. As you might expect from the name, the Table service uses a tabular format to store data.In the standard terminology, each row of the table represents an entity, and the columns store the various properties of that entity.Tip The content in this article applies to the original Azure Table storage.However, there is now a premium offering for table storage, the Azure Cosmos DB Table API that offers throughput-optimized tables, global distribution, and automatic secondary indexes.There are some feature differences between Table API in Azure Cosmos DB and Azure table storage, to learn more and try out the premium experience, please check out Azure Cosmos DB Table API.To design scalable and performant tables you must consider a number of factors such as performance, scalability, and cost.As well as being part of the addressing scheme for entities, partitions define a scope for transactions (see Entity Group Transactions below), and form the basis of how the table service scales.For more information on partitions, see Azure Storage Scalability and Performance Targets.
A well designed No SQL data store can enable your solution to scale much further (and at a lower cost) than a solution that uses a relational database. This section highlights some of the key features of the Table service that are especially relevant to designing for performance and scalability.
So far, this design looks similar to a table in a relational database with the key differences being the mandatory columns, and the ability to store multiple entity types in the same table.
In addition, each of the user-defined properties such as First Name or Age has a data type, such as integer or string, just like a column in a relational database.
Every entity has a pair of keys to uniquely identify it, and a timestamp column that the Table service uses to track when the entity was last updated (the timestamp field is added automatically and you cannot manually overwrite the timestamp with an arbitrary value).
The Table service uses this last-modified timestamp (LMT) to manage optimistic concurrency.